Oscar De Silva
Papers
10
Total Citations
156
H-Index
7
About
Oscar De Silva is a leading researcher in multi-robot systems, sensor fusion, and autonomous navigation, with a particular focus on relative localization for heterogeneous robot teams. His pioneering work on ultrasonic and vision-based relative positioning sensors has enabled robust, low-cost spatial localization in multi-robot networks, earning over 50 citations. De Silva has made foundational contributions to ground-aerial robot coordination, developing pairwise observable localization schemes that allow dynamic agents to operate with minimal communication—a critical advancement for real-world deployments. His research on efficient distributed localization, inspired by target tracking, has addressed key challenges in asynchronous communication, with papers accumulating hundreds of citations. Notably, De Silva led the creation of the MUN-FRL dataset, a comprehensive visual-inertial-LiDAR resource for GNSS-denied aerial navigation, already cited 16 times since its 2024 release. He has also advanced attitude estimation through invariant nonlinear complementary filters, offering low-computational solutions for low-cost platforms. His work on factor graph localization using Google Indoor Street View and CNN-based place recognition demonstrates his commitment to bridging simulation and real-world autonomy. With over 150 citations across his portfolio, De Silva continues to shape the future of collaborative robotics and autonomous systems.
Research Focus
Key Achievements
Top Papers
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- 7Relative localization with symmetry preserving observers7 citations · 2014
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